System and method for efficiently identifying a subject

ABSTRACT

System and Method for Efficiently Identifying a Subject 
     A system and a method for efficiently identifying a subject is provided. The invention provides for segmenting micro-voltage digital signals into intervals of a pre-defined time period. Further, the invention provides for transforming the segmented micro-voltage digital signals into a frequency domain for computing on a Mel&#39;s scale. The Mel&#39;s scale provides a unique signature of the subject in the form of a Melspectrogram image. Lastly, the invention provides for passing the Melspectrogram image through a trained deep learning model. The features associated with the Melspectrogram image are extracted into a feature map for obtaining predicted labels associated with the subject based on labels used during training of the deep learning model for identifying the subject.

FIELD OF THE INVENTION

The present invention relates generally to the field of subjectidentification. More particularly, the present invention relates to asystem and a method for efficiently identifying a subject based onmicro-vibrations generated by the subject's body.

BACKGROUND OF THE INVENTION

Identification of a subject (i.e. a person) accurately for effectivecontactless health monitoring is essential as it aids in maintaining thesubject's health data correctly and adequately. Contactless andnon-invasive health monitoring techniques provide capturing health dataof a subject without altering the subject's lifestyle or livingenvironment by using a contactless health monitoring device. Further,the use of telemedicine in contactless health monitoring has increaseddue to worsening of subjects to caregivers ratio and therefore effectiveand correct health data of a subject is required for monitoring thesubject remotely for providing proper treatment.

However, there may be a scenario in which more than one subject ispresent near the contactless health monitoring device and the healthdata of an intended subject may not be captured effectively andcorrectly by the health monitoring device, as the health data of anon-intended subject may also be captured and tagged along with theintended subject's health data. It has been observed that the existinghealth monitoring techniques and devices capture health data fornon-intended subject, who may be in proximity or in place of theintended subject and incorrectly mark the non-intended subject as theintended subject, therefore providing discrepancy in the captured healthdata.

Further, the health data is used for various purposes such as, trackinga subject's health deterioration, health patterns, heart rate, bloodpressure, sleep patterns, maintaining health data and records,compliance to health plans, etc. It has been observed that the existinghealth monitoring techniques are not able to monitor the subject'shealth efficiently and are usually invasive. Further, the health datacaptured by the existing health monitoring techniques are prone totampering, theft and fraud (e.g. the intended subject may record thehealth data for some other subject and mark it as its own). Furthermore,authenticating techniques (e.g. video and computer vision, fingerprints,biometrics, etc.) used for determining the identity of the intendedsubject are usually expensive, difficult to maintain and are not secure.The biometrics data (e.g. fingerprints, retina scans, DNA data, etc.)used in authenticating techniques may be replicated (e.g. fingerprintsmay easily be taken off from a surface using “gummy fingers”) and hencemay be used without the subject's knowledge and consent.

In light of the above mentioned drawbacks, there is a need for a systemand a method which provides for efficiently identifying a subject.Further, there is a need for a system and a method which provides forcorrectly capturing health data of the subject. Furthermore, there is aneed for a system and a method which provides for non-invasive andsecure authentication techniques for identifying the subject.

SUMMARY OF THE INVENTION

In various embodiments of the present invention, a system forefficiently identifying a subject is provided. The system comprising amemory storing program instructions, a processor configured to executeinstructions stored in the memory and an identification engine executedby the processor. The identification engine is configured to segmentmicro-voltage digital signals into intervals of a pre-defined timeperiod. Further, the identification engine is configured to transformthe segmented micro-voltage digital signals into a frequency domain forcomputing on a Mel's scale. The Mel's scale provides a unique signatureof the subject in the form of a Melspectrogram image. Lastly, theidentification engine is configured to pass the Melspectrogram imagethrough a trained deep learning model. The features associated with theMelspectrogram image are extracted into a feature map for obtainingpredicted labels associated with the subject based on labels used duringtraining of the deep learning model for identifying the subject.

In various embodiments of the present invention, a method forefficiently identifying a subject is provided. The method is implementedby a processor executing instructions stored in a memory. The methodcomprises segmenting micro-voltage digital signals into intervals of apre-defined time period. Further, the segmented micro-voltage digitalsignals are transformed into a frequency domain for computing on a Mel'sscale. The Mel's scale provides a unique signature of the subject in theform of a Melspectrogram image. Lastly, the Melspectrogram image ispassed through a trained deep learning model. The features associatedwith the Melspectrogram image are extracted into a feature map forobtaining predicted labels associated with the subject based on labelsused during training of the deep learning model for identifying thesubject.

In various embodiments of the present invention, a computer programproduct is provided. The computer program product comprises anon-transitory computer-readable medium having computer program codestored thereon, the computer-readable program code comprisinginstructions that, when executed by a processor, causes the processor tosegment micro-voltage digital signals into intervals of a pre-definedtime period. The processor further transform the segmented micro-voltagedigital signals into a frequency domain for computing on a Mel's scale.The Mel's scale provides a unique signature of the subject in the formof a Melspectrogram image. Lastly, the processor further pass theMelspectrogram image through a trained deep learning model. The featuresassociated with the Melspectrogram image are extracted into a featuremap for obtaining predicted labels associated with the subject based onlabels used during training of the deep learning model for identifyingthe subject.

Brief description of the accompanying drawings

The present invention is described by way of embodiments illustrated inthe accompanying drawings wherein:

FIG. 1 is a detailed block diagram of a system for efficientlyidentifying a subject, in accordance with an embodiment of the presentinvention;

FIG. 2 illustrates captured ballistocardiographic (BCG) signalsgenerated by a subject's body, in accordance with an embodiment of thepresent invention;

FIG. 3 illustrates a Melspectrogram in a frequency domain in a range ofbetween 0 Hz-45Hz, in accordance with an embodiment of the presentinvention;

FIG. 4 illustrates a deep learning model architecture, in accordancewith an embodiment of the present invention;

FIG. 5 illustrates initial six filters from a first convolutional layerof the deep learning model architecture, in accordance with anembodiment of the present invention;

FIG. 6 illustrates a feature map of a transformed Melspectrogram imageassociated with a subject 1 after passing through the filters of theconvolution layer, in accordance with an embodiment of the presentinvention;

FIG. 7 illustrates a feature map of a transformed Melspectrogram imageassociated with a subject 2 after passing through the filters of theconvolution layer, in accordance with an embodiment of the presentinvention;

FIG. 8 a and FIG. 8 b illustrates a flow of the Melspectrogram image ofthe subject 1 and the subject 2, which is provided to the convolutionlayers of the trained deep learning model and is passed through thefilters of the convolution layers for providing an output image, inaccordance with an embodiment of the present invention;

FIG. 9 illustrates a confusion matrix in a grid form providing trainingaccuracy and execution accuracy of the deep learning model for over 20subjects, in accordance with an embodiment of the present invention;

FIG. 10 and FIG. 10A illustrates a method for efficiently identifying asubject, in accordance with an embodiment of the present invention; and

FIG. 11 illustrates an exemplary computer system in which variousembodiments of the present invention may be implemented.

DETAILED DESCRIPTION OF THE INVENTION

The present invention discloses a system and a method for efficientlyidentifying a subject (i.e. a person). In particular, the presentinvention provides for correctly identifying a subject based onmicro-vibrations generated by the subject's body. Further, the presentinvention provides for effective health monitoring and generating healthdata of the identified subject. Further, the present invention providesfor a system and a method for a non-invasive and a double layer secureauthentication for identifying the subject. Furthermore, the inventionprovides for a system and a method for identification of a subject in acost effective manner.

The disclosure is provided in order to enable a person having ordinaryskill in the art to practice the invention. Exemplary embodiments hereinare provided only for illustrative purposes and various modificationswill be readily apparent to persons skilled in the art. The generalprinciples defined herein may be applied to other embodiments andapplications without departing from the scope of the invention. Theterminology and phraseology used herein is for the purpose of describingexemplary embodiments and should not be considered limiting. Thus, thepresent invention is to be accorded the widest scope encompassingnumerous alternatives, modifications and equivalents consistent with theprinciples and features disclosed herein. For purposes of clarity,details relating to technical material that is known in the technicalfields related to the invention have been briefly described or omittedso as not to unnecessarily obscure the present invention.

The present invention would now be discussed in context of embodimentsas illustrated in the accompanying drawings.

FIG. 1 is a detailed block diagram illustrating a system 100 forefficiently identifying a subject.

In an embodiment of the present invention, the system 100 is configuredto capture micro-vibrations generated by a subject's (i.e. a person)body for identifying the said subject. The micro-vibrations aregenerated due to myoelectric properties of the cardiovascular systems.The micro-vibrations are associated with ballistocardiographic (BCG)signals. The micro-vibrations associated with the BCG signals, generatedby the subject's body, are unique in nature and no two subjects havesame micro-vibrations. Further, the system 100 operates in a non-contactand non-invasive manner and evolves based on the subject's identitydata.

In an embodiment of the present invention, the system 100 comprises asensor device 102, a data capturing subsystem 104, a data receiverdevice 118, an identification subsystem 120 and a user device 134.

In an embodiment of the present invention, the sensor device 102comprises an array of sensors which are placed in a housing at thesubject's end. The sensor device 102 is specifically designed forcarrying out various embodiments of the present invention. In anexemplary embodiment of the present invention, the sensor device 102 isof a very low thickness, preferably of around 3mm and has an outercasing for protecting and covering the housing. The outer casing may bea robust and rugged thin cover made of a material, (e.g. mesh, latex,cloth, polymer etc.) that firmly holds the array of sensors in a fixedposition. In another exemplary embodiment of the present invention, thesensor device 102 comprises, vibroacoustic sensors, piezoelectricsensors, etc. for capturing and amplifying micro-vibrations generated bythe subject's body. The sensor device 102 may be of particular shapesand sizes that may include, but is not limited to, rectangular, square,circular, oval etc. The sensor device 102 is capable of being folded andis a lightweight device. In various embodiments of the presentinvention, the sensor device 102 is used in a non-invasive andcontactless manner. The sensor device 102 may be placed under a mediumsuch as a mattress, cushion etc. on which the subject may sit, stand,lie down or sleep. The sensor device 102 may be aligned in any restingposition such as, but is not limited to, sitting position, lying downposition etc. with respect to the subject.

In an embodiment of the present invention, the sensor device 102,positioned in a contactless manner at the subject's end, is configuredto capture micro-vibrations generated by the subject's body as analogdata signals. The sensor device 102 is capable of capturingmicro-vibrations received through a medium placed between the subjectand sensor device 102. For example, the micro-vibrations may be capturedthrough a medium ranging from a thin surface to a thick surface such asa 20-inch mattress. The micro-vibrations captured by the sensor device102 may include, but are not limited to, ballistocardiographic (BCG)signals, as illustrated in FIG. 2 , such as, cardiac cycles or heartrates, heart movements, chest movements, body movements, respiration(pulmonary) signals etc. Further, the sensor device 102 is configured toconvert the captured micro-vibrations, which are analog signals, intomicro-voltage digital signals, which are further amplified.

In an embodiment of the present invention, the data capturing subsystem104 is configured to receive the micro-voltage digital signals from thesensor device 102. The sensor device 102 is connected to the datacapturing subsystem 104 via a wired or wireless connection. The datacapturing subsystem 104 may be positioned at the subject's location. Invarious embodiments of the present invention, the data capturingsubsystem 104 comprises a data capturing engine 106, a processor 114 anda memory 116. The data capturing engine 106 comprises multiple unitsthat operate in conjunction with each other for capturing, processingand transmitting the data received from the sensor device 102 to thedata receiver unit 118. The various units of the data capturing engine106 are operated via the processor 114 specifically programmed toexecute instructions stored in the memory 116 for executing respectivefunctionalities of the units of the engine 106 in accordance withvarious embodiments of the present invention.

In an embodiment of the present invention, the data capturing engine 106comprises a data acquisition unit 108, a conditioning unit 110 and atransmission unit 112.

In an embodiment of the present invention, the data acquisition unit 108of the data capturing engine 106 is configured to receive themicro-voltage digital signals from the sensor device 102 and record thereceived micro-voltage digital signal in a pre-defined data recordingformat. The pre-defined data recording format may include, but is notlimited to, a chronological format.

In an embodiment of the present invention, the data acquisition unit 108transmits the recorded micro-voltage digital signals to the conditioningunit 110. The conditioning unit 110 is configured to amplify themicro-voltage digital signals for maximizing resolution of themicro-voltage digital signals, as desired, to accurately process themicro-voltage digital signals. The maximization of resolution ofmicro-voltages digital signal is carried out without data loss orinformation loss that may occur due to clipping. Advantageously,amplification and resolution maximization of the micro-voltage digitalsignal aids the sensor device 102 to operate with any thickness andconstruction of medium between the sensor device 102 and the subject.The conditioning unit 110 is configured with multiple amplificationcapabilities for amplifying the micro-voltage digital signals dependingupon the strength of the received micro-voltage digital signals from thedata acquisition unit 108. In an exemplary embodiment of the presentinvention, the multiple amplification capabilities embedded in theconditioning unit 110 provides, but are not limited to, eight differentamplification options that amplify the micro-voltages between the rangeof 15× to 2500×. The conditioning unit 110 is configured toautomatically calibrate and select the amplification option. Theconditioning unit 110 is based on a sensitivity shifting mechanism forautomatically calibrating and selecting the amplification option. Thesensitivity shifting mechanism depends upon the level of strength of themicro-voltage digital signals received from the sensor device 102.

In an embodiment of the present invention, the transmission unit 112 ofthe data capturing engine 106 is configured to receive and transmit theamplified micro-voltage digital signal to the data receiver 118 atregular time intervals. The amplified micro-voltage digital signal istransmitted to the data receiver unit 118 via a communication channel(not shown). The communication channel (not shown) may include, but isnot limited to, a wire or a logical connection over a multiplexedmedium, such as, a radio channel in telecommunications and computernetworking. Examples of telecommunications and computer networking mayinclude a local area network (LAN), a metropolitan area network (MAN), awide area network (WAN) or any wired or wireless network, such as, butis not limited to, Wi-Fi, Bluetooth Classic and Bluetooth Low Energy. Inan exemplary embodiment of the present invention, the data receiver unit118 may be positioned at the location of the sensor device 102 and thedata capturing subsystem 104. For example, the data receiver unit 118may be installed on a smartphone, tablet, laptop, computer system etc.of the subject. In another exemplary embodiment of the presentinvention, the data receiver unit 118 may be positioned at a locationremote to the sensor device 102 and the data capturing subsystem 104,such as, in a cloud based server. In an embodiment of the presentinvention, the data receiver unit 118 is configured to store themicro-voltage digital signals in a pre-defined data storage format,which may include, but is not limited to, a chronological format in theform of datasets.

In an embodiment of the present invention, the data receiver unit 118communicates with the identification subsystem 120. The identificationsubsystem 120 comprises an identification engine 122, a processor 124and a memory 126. In an embodiment of the present invention, theidentification engine 122 comprises multiple units that operate inconjunction with each other for processing the data received from thedata receiver unit 118. The various units of the data capturing engine122 are operated via the processor 124 specifically programmed toexecute instructions stored in the memory 126 for executing respectivefunctionalities of the units of the engine 122 in accordance withvarious embodiments of the present invention.

In an embodiment of the present invention, the identification engine 122comprises a computation unit 128, a prediction unit 130 and a database132.

In an embodiment of the present invention, the computation unit 128 isconfigured to receive the micro-voltage digital signals from the datareceiver unit 118. The computation unit 128 is configured to process thereceived micro-voltage digital signals for segmenting into intervals ofa pre-defined time period. In an exemplary embodiment of the presentinvention, intervals of the pre-defined time period comprises ±10seconds. In an embodiment of the present invention, the computation unit128 is configured to transform the segmented micro-voltage digitalsignals into a frequency domain in order to compute the said frequencydomain on a Mel's scale. The computation of frequency domain on theMel's scale provides a unique signature of the subject in the form of aMelspectrogram image, as illustrated in FIG. 3 . FIG. 3 illustrates theMelspectrogram image in the frequency domain in a range of between0Hz-45Hz.

In an embodiment of the present invention, the prediction unit 130 ofthe identification engine 122 is configured to receive the computedMelspectrogram images from the computation unit 128. The prediction unit130 is configured to analyze and process the Melspectrogram images forefficiently predicting the identity of the subject. The prediction unit130 uses cognitive computing techniques such as, but are not limited to,deep learning techniques for predicting the identity of the subjectbased on analysis and processing of the Melspectrogram image. The deeplearning techniques comprise neural networks which may include, but arenot limited to, a Deep Neural Network (DNN), a Long Short Term MemoryNetwork (LSTM) and a Convolutional Neural Network (CNN). In anembodiment of the present invention, the prediction unit 130 isconfigured to generate a deep learning model using the neural networksassociated with the deep learning techniques for efficiently predictingthe identity of the subject from the Melspectrogram image. Thearchitecture of the deep learning model comprises pre-defined number ofneural network layers, which are stacked together, through which theMelspectrogram image is passed in order to extract features from theMelspectrogram images for identifying the subject. The features areassociated with the subject.

In an exemplary embodiment of the present invention, the pre-definednumber of neural network layers of the deep learning model architecturecomprises three convolution 2-D layers paired with three max pooling 2-Dlayers respectively, two dense layers, a flattening layer between thetwo dense layers and a dropout layer, as illustrated in FIG. 4 . Thenumber and sequence of neural network layers of the deep learning modelarchitecture may vary, in accordance with various embodiments of thepresent invention. In an embodiment of the present invention, the threeconvolution 2-D layers which are paired with max pooling 2-D layers areused for extracting required features in the form of a feature map fromthe Melspectrogram images and for subsequently carrying outdownsampling. The flattening layer provides passing of a 1-D tensorassociated with the Melspectrogram images to the dense layer. Further,the dropout layer between the two dense layers aids in preventing thedeep learning model from over fitting.

In an embodiment of the present invention, the deep learning model isgenerated using pre-defined number of neural network layers, which issubsequently trained with multiple training datasets prior to predictingthe identity of the subject. The training datasets are associated withmultiple subjects and generated based on capturing the subject'smicro-vibrations, when the subject is in a resting position (e.g.sleeping) and converted into the Melspectrogram image. In an embodimentof the present invention, the training datasets used for trainingrelates to different subjects. Multiple datasets of different subjectsare provided to the prediction unit 130 for training the deep learningmodel. In an embodiment of the present invention, the training datasetsare pre-processed and inputted to the prediction unit 130 along withlabels for training the deep learning model. Labels used in training ofthe deep learning model represent ground truth associated with eachMelspectrogram image. In an embodiment of the present invention, thetraining datasets comprise input images associated with multiplesubjects, the input images are Melspectrogram images in a 4-D format,with dimensions “batch_size, height, width, depth”, such thatbatch_size=a number of training images in one forward pass; height(H)=height of the image; width (W)=width of the image; and depth(D)=number of color channels of the image.

In an embodiment of the present invention, during training, theprediction unit 130 trains the deep learning model by passing theMelspectrogram images through the convolution layers and max pooling 2-Dlayers along with the respective labels. The batch_size of the outputimages remains same as that of input Melspectrogram images, while theother dimensions (i.e. height (H), width (W) and depth (D)) change basedon number of filters, kernels and padding of the convolution layers. Inan exemplary embodiment of the present invention, the filters of theconvolution layer of the deep learning model architecture compriseslight color regions (e.g. yellow regions) and dark color regions (e.g.dark blue regions), as illustrated in FIG. 5 , such that the light color(yellow) region in the filter represents a value ‘1’, and the dark color(blue) region in the filter represents a value ‘0’. FIG. 5 illustratesinitial six filters from the convolutional layer of the deep learningmodel architecture.

In an embodiment of the present invention, the prediction unit (130)passes the Melspectrogram image through the three convolution 2-D layerspaired with three max pooling 2-D layers by providing the Melspectrogramimage as an input to a first convolution layer and the output of thefirst convolution layer is provided as an input to the first max pooling2-D layer. The first max pooling 2-D layer generates an output imagewith further modified dimensions, which is provided as an input to asecond convolution layer and output of the second convolution layer isprovided as an input to a second max pooling 2-D layer. The output ofthe second max pooling 2-D layer is provided as an input to a thirdconvolution layer and output of the third convolution layer is providedas an input to a third max pooling 2-D layer, as illustrated in FIG. 4 .Therefore, multiple Melspectrogram images associated with differentsubjects are provided for training the deep learning model and the deeplearning model is trained based on providing different trainingdatasets.

In an embodiment of the present invention, in operation, subsequent totraining of the deep learning model, the trained deep learning model isimplemented for computing the identity of the subject. A Melspectrogramimage associated with a subject is passed through the trained deeplearning model for computing the identity of the subject. The traineddeep learning model is configured to extract features associated withthe Melspectrogram image into a feature map to obtain predicted labelsassociated with the subject. The predicted labels are obtained based onlabels used for training the deep learning model and for computing theidentity of the subject. The trained deep learning model classifies theMelspectrogram image associated with the subject based on the trainedlabels in order to identify the subject. In an embodiment of the presentinvention, the input Melspectrogram image, associated with the subject,is pre-processed for computing a Melspectrogram image of a dimension“None, 32, 32, 3” which is provided as an input to the first convolutionlayer of the deep learning model by the prediction unit 130. Thedimension ‘None’ represents various numbers of images which are providedwhile training and therefore represents a batch size. “32, 32, and 3”represents height (H1), width (W1) and depth (D1) respectively of theMelspectrogram image. The first convolution layer thereafter generatesan output of a dimension “None, 30, 30, 16”. In an exemplary embodimentof the present invention, the output is generated by the firstconvolution layer based on the following computation: as the inputMelspectrogram image is of a dimension “None, 32, 32, 3” and if thenumber of filters (K1) used in first convolution layer is 16, strides(S) is 1 and spatial extent of filters (F) is 3 with 0 padding (P), thenthe output generated by first convolution layer is computed as:H1=(W1−F+2*P)/S+1; W1=(W1−F+2*P)/S+1; and D1=K1. Thus,H1=(32−3+1*0)/1+1=30; W1=(32−3+1*0)/1+1=30; and D1=16. Strides representa parameter of the neural network's filter that modifies the amount ofmovement over the image pixel. Therefore, the output generated by thefirst convolution layer is of the dimension “None, 30, 30, 16”. Further,the output from the first convolution layer is provided as an input tothe first max pooling 2-D layer, which uses a shape of dimensions (2, 2)(i.e. (H, W)), for reducing dimensions of the output received from thefirst convolution layer for generating an output of a dimension “None,15, 15, 16”, such that, ‘None’ represents various numbers of imageswhich are provided while training and 15, 15 and 16 represents height,width and depth of the output. Further, the output from the first maxpooling 2-D layer is provided as an input to the second convolutionlayer. The second convolution layer generates an output of a dimension(None, H2, W2, D2) based on the following computation:H2=(15−3+1*0)/1+1=13; W2=(15−3+1*0)/1+1=13; and D2=16. Therefore, theoutput generated by the second convolution layer is of a dimension“None, 13, 13, 16”, such that, ‘None’ represents various numbers ofimages which are provided while training and “13, 13, 16” representsheight (H2), width (W2) and depth (D2) of the output. Further, theoutput from the second convolution layer is provided as an input to thesecond max pooling 2-D layer, which reduces the dimension of the inputto a dimension “None, 6, 6, 16”, such that, ‘None’ represents variousnumbers of images which are provided while training and “6, 6, 16”represents height, width and depth of the output. Similarly, the outputfrom the second max pooling 2-D layer is provided to the thirdconvolution layer for generating an output of a dimension “None, 4, 4,16”, wherein ‘None’ represents various numbers of images which areprovided while training and “4, 4, 16” represents height (H3), width(W2) and depth (D3) of the output. Further, the output from the thirdconvolution layer is provided as an input to a third max pooling 2-Dlayer, which generates an output of a dimension “None, 2, 2, 16”, suchthat, ‘None’ represents various numbers of images which are providedwhile training and “2, 2, 16” represents height, width and depth of theoutput. The output from the third max pooling 2-D layer is provided to aflattening layer, which multiplies the dimensions together as (2*2*16)for generating an output of a dimension “None, 64”. The output from theflattening layer is provided as an input to the first dense layer, forgenerating an output of a dimension “None, 256”. Further, the outputfrom the first dense layer is provided as an input to a dropout layerfor generating an output of a dimension “None, 256”. Further, the outputfrom the dropout layer is provided as an input to the second dense layerfor generating as an output of a dimension “None, 20”, and value “20”represents number of labels. Further, the output from the second denselayer is associated with the predicted label used for identifying thesubject. The predicted labels represent response of the trained deeplearning model for classification of the Melspectrogram image associatedwith the new subject. The output of every layer of the trained deeplearning model, having different weights, is provided in Table 1, asillustrated below:

TABLE 1 Layer (type) Output Shape Param # conv2d_

 (Conv20) (None,

,

,

)

max_pooling2d_16 (Maxpooling (None, 15, 15,

) 0 conv2d_17 (Conv20) (None, 13, 13,

)

max_pooling2d_17 (MaxPooling (None, 6, 6,

) 0 conv2d_18 (Conv20) (None, 4, 4,

)

max_pooling2d_18 (MaxPooling (None, 2, 2,

) 0 flatten_

 (Flatten) (None, 64) 0 dense_1 (Dense) (None, 256)

dropout_

 (Dropout) (None, 256) 0 dense_2 (Dense) (None,

)

Total params: 

Trainable params: 

Non-trainable params: 0

indicates data missing or illegible when filed

In an embodiment of the present invention, the prediction unit 130 isconfigured to compute parameters (Param#) by implementing the firstconvolution layer, the second convolution layer, the third convolutionlayer, the first dense layer and the second dense layer of the traineddeep learning model, as illustrated in Table 1. The parameters representthe number of learnable elements in a convolution layer and are alsocomputed during the training of the deep learning model, prior to theimplementation of the trained deep learning model. In an embodiment ofthe present invention, the number of parameters are computed based onthe number of filters (K) used along with their kernel size (KZ), a biasand number of filters in the previous layer (D) using the followingcomputation: Number of Parameters (Param#)=K (D*(KZ)+1). In an exemplaryembodiment of the present invention, if in the first convolution layerthe number of filters (K) are 16 and the kernel size (KZ) is (3, 3),then the number of parameters for the first convolution layer is: Param#1=16*(3*(3*3)+1)=448. Further, for the second convolution layer, thenumber of parameters (Param #2) is: Param #2=16*(16*(3 *3)+1)=2320. Yetfurther, for the third convolution layer, the number of parameters(Param #3) is: Param #3=16 *(16*(3*3)+1)=2320. For the first denselayer, the number of parameters (Param #4) is: Param#4=256*(64+1)=16640;and further for second dense layer, the number of parameters (Param #5)is: Param #5=20*(256+1)=5140. Therefore, number of total parameters is26,868 (i.e. 448+2320+2320+16640+5140), and number of trainableparameters is 26,868, as illustrated in Table 1. The trainableparameters represent the parameters used for training the deep learningmodel.

In an embodiment of the present invention, the prediction unit 130 isconfigured to transform the output from the second dense layerassociated with a subject's Melspectrogram image (e.g. subject 1 and asubject 2) in a feature map for obtaining predicted labels byimplementing the convolution layer filters present in the convolutionlayer of the trained deep learning model, as illustrated in FIG. 6 andFIG. 7 respectively, in order to identify the subject. Further, FIG. 6and FIG. 7 illustrates feature map which is generated based on passingthe Melspectrogram image through the convolution layer filters (asillustrated in FIG. 5 ). The feature maps presented in FIG. 6 and FIG. 7capture the result of applying the filters as shown in FIG. 5 to theinput i.e. the Melspectrogram image. In an embodiment of the presentinvention, the trained deep learning model is applied for differentsubjects for identifying and distinguishing one subject (subject 1) fromanother subject (subject 2). The Melspectrogram image of a particularsubject is provided to the convolution layers of the trained deeplearning model, which is passed through the filters of the convolutionlayers, and provided as the feature map for distinguishing andidentifying the subject 1 and the subject 2 respectively, as illustratedin a flow diagram in FIG. 8 a and FIG. 8 b . The trained model providesaccuracy and sensitivity in identifying and distinguishing one subjectfrom another subject based on computing the identity of the subject.Advantageously, the training accuracy of the deep learning model forover 20 subjects is computed to be 86.65% with execution accuracy of56%, as illustrated in a confusion matrix in FIG. 9 . The accuracy ofthe deep learning model is determined based on comparing predictedlabels with the ground truth labels. Further, FIG. 9 represents theconfusion matrix providing the data associated with validation accuracyof the deep learning model where rows represent actual or true labelsand columns represent the predicted labels. Further, higher the densityof diagonal in the confusion matrix, greater is the validation accuracyof the deep learning model.

In an embodiment of the present invention, the prediction unit 130 isconfigured to transmit identity data associated with the computedidentity of the subject to the database 132 for storage and futureretrieval. The database 132 may be located locally or remotely withrespect to the identification subsystem 120. The database 132 may belocated locally on a standalone smartphone, laptop, tablet, a desktopcomputer, etc. at the subject's end. The database 132 may be locatedremotely on a cloud server. In an embodiment of the present invention,the user device 134 is configured to connect to the database 132 forretrieving the stored subject's identity data. The user device 134 mayinclude, but is not limited to, a smartphone, a tablet, a smartwatch, acomputed system and a laptop. The subject may download an application onthe user device 134 or use a web address (e.g. a Universal ResourceLocator (URL)) in order to connect to the database 132 for retrieving,accessing and viewing the subject's identity data. Further, theapplication and the web address provides a Graphical User Interface(GUI) in a dashboard form for viewing the subject's identity data. In anembodiment of the present invention, the user device 134 via theapplication or the web address is configured to uniquely authorize eachsubject by registering and providing access to subjects for viewing thestored identity data. Further, the subject's identity data may beaccessed by at least, the subject itself, subject's doctor, subject'scaretaker, an insurer or any other person related to subject in order tocorrectly and effectively determine the identity of the subject.

In an embodiment of the present invention, the identification subsystem120 is further configured to compute health data of the subject based onthe identity data of the subject. Further, the health data of anon-intended subject which may have been captured and taggedintentionally or unintentionally along with the health data of theintended subject is removed based on the identity data of the identifiedsubject. An intended subject is a subject, whose health data is requiredto be captured and a non-intended subject is a subject, whose healthdata is not required to be captured. Further, the health data capturedfrom an intended subject and a non-intended subject is distinguished forpreventing mixing of the health data of the intended subject and thenon-intended subject.

In an embodiment of the present invention, the identification subsystem120 is configured to couple the identity data of the identified subjectwith subject's biometric data (e.g. subject's retina scan, subject'sfingerprints etc.) for providing a double layer secure authentication.For example, an insurer may use subject's biometric data for identifyingthe subject in order to provide any special offers, however, thebiometric data of the said subject may be manipulated by another subjectand the benefits of the insurance may be wrongly appropriated by thesaid another subject. Coupling of subject's identity data, computed inaccordance with various embodiments of the present invention, with thesubject's biometric data prevents such wrong appropriation, as theidentity data of a particular subject is unique to the said subject andcannot be manipulated.

FIG. 10 and FIG. 10A illustrates a method for efficiently identifying asubject, in accordance with various embodiments of the presentinvention.

At step 1002, micro-vibrations generated by a subject's body arecaptured and converted into micro-voltage digital signals. In anembodiment of the present invention, micro-vibrations generated by thesubject's body are captured as analog data signals. The capturedmicro-vibrations may include, but are not limited to,ballistocardiographic (BCG) signals such as, cardiac cycles or heartrates, heart movements, chest movements, body movements, respiration(pulmonary) signals etc. Further, the captured micro-vibrations, whichare analog signals, are convert into micro-voltage digital signals.

At step 1004, the micro-voltage digital signals are amplified. In anembodiment of the present invention, the micro-voltage digital signalsare recorded in a pre-defined data recording format. The pre-defineddata recording format may include, but is not limited to, achronological format. Further, the recorded micro-voltage digitalsignals are amplified for maximizing resolution of the micro-voltagedigital signals, as desired, to accurately process the micro-voltagedigital signals. The maximization of resolution of micro-voltagesdigital signal is carried out without data loss or information loss thatmay occur due to clipping. The micro-voltage digital signals areamplified depending upon the strength of the received micro-voltagedigital signals. In an exemplary embodiment of the present invention,the amplification capabilities provides, but are not limited to, eightdifferent amplification options that amplify the micro-voltages betweenthe range of 15× to 2500×. The amplification option is automaticallycalibrated and selected. Further, a sensitivity shifting mechanism isused for automatically calibrating and selecting the amplificationoption. The sensitivity shifting mechanism depends upon the level ofstrength of the micro-voltage digital signals.

In an embodiment of the present invention, the amplified micro-voltagedigital signal are transmitted regular time intervals via acommunication channel for storage in a pre-defined data storage format,which may include, but is not limited to, a chronological format in theform of datasets.

At step 1006, the micro-voltage digital signals are segmented intointervals of a pre-defined time period. In an embodiment of the presentinvention, intervals of the pre-defined time period comprises ±10seconds.

At step 1008, the segmented micro-voltage digital signals aretransformed into a frequency domain in order to compute in the form of aMelspectrogram image. In an embodiment of the present invention, thesegmented micro-voltage digital signals are transformed into a frequencydomain in order to compute the said frequency domain on a Mel's scale.The computation of frequency domain on the Mel's scale provides a uniquesignature of the subject in the form of a Melspectrogram image.

At step 1010, a deep learning model is generated and trained foridentifying the subject by analyzing and processing the Melspectrogramimage. In an embodiment of the present invention, the Melspectrogramimages are analyzed and processed for efficiently predicting theidentity of the subject. Further, cognitive computing techniques suchas, but are not limited to, deep learning techniques are used forpredicting the identity of the subject and effectively capturing thehealth data of the subject based on analysis and processing of theMelspectrogram. The deep learning techniques comprise neural networkswhich may include, but are not limited to, a Deep Neural Network (DNN),a Long Short Term Memory Network (LSTM) and a Convolutional NeuralNetwork (CNN). In an embodiment of the present invention, a deeplearning model is generated using the neural networks associated withthe deep learning techniques for efficiently predicting the identity ofthe subject from the Melspectrogram image. The architecture of the deeplearning model comprises pre-defined number of neural network layers,which are stacked together, through which the Melspectrogram image ispassed in order to extract features from the Melspectrogram images foridentifying the subject. The features are associated with the subject.

In an exemplary embodiment of the present invention, the pre-definednumber of neural network layers of the deep learning model architecturecomprises three convolution 2-D layers paired with three max pooling 2-Dlayers respectively, two dense layers, a flattening layer between thetwo dense layers and a dropout layer. The number and sequence of neuralnetwork layers of the deep learning model architecture may vary, inaccordance with various embodiments of the present invention. In anembodiment of the present invention, the three convolution 2-D layerswhich are paired with max pooling 2-D layers are used for extractingrequired features in the form of a feature map from the Melspectrogramimages and for subsequently carrying out downsampling. The flatteninglayer provides passing of a 1-D tensor associated with theMelspectrogram images to the dense layer. Further, the dropout layerbetween the two dense layers aids in preventing the deep learning modelfrom over fitting.

In an embodiment of the present invention, the deep learning model isgenerated using pre-defined number of neural network layers, which issubsequently trained with multiple training datasets prior to predictingthe identity of the subject. The training datasets are associated withmultiple subjects and generated based on capturing the subject'smicro-vibrations, when the subject is in a resting position (e.g.sleeping) and converted into the Melspectrogram image. In an embodimentof the present invention, the training datasets used for trainingrelates to different subjects. Multiple datasets of different subjectsare provided for training the deep learning model. In an embodiment ofthe present invention, the training datasets are pre-processed andinputted along with labels for training the deep learning model. Labelsused in training of the deep learning model represent ground truthassociated with every Melspectrogram image. In an embodiment of thepresent invention, the training datasets comprises input imagesassociated with multiple subjects, the input images are Melspectrogramimages in a 4-D format, with dimensions “batch_size, height, width,depth”, such that batch_size=a number of training images in one forwardpass; height (H)=height of the image; width (W)=width of the image; anddepth (D)=number of color channels of the image.

In an embodiment of the present invention, during training, the deeplearning model is trained by passing the Melspectrogram images throughthe convolution layers and max pooling 2-D layers along with therespective labels. The batch_size of the output images remains same asthat of input Melspectrogram image while the other dimensions (i.e.height (H), width (W) and depth (D)) change based on number of filters,kernels and padding of the convolution layers. In an exemplaryembodiment of the present invention, the filters of the convolutionlayer of the deep learning model architecture comprises light colorregions (e.g. yellow regions) and dark color regions (e.g. dark blueregions), such that, the light color (yellow) region in the filterrepresents a value ‘1’, and the dark color (blue) region in the filterrepresents a value ‘0’.

In an embodiment of the present invention, the Melspectrogram image ispassed through the three convolution 2-D layers paired with three maxpooling 2-D layers by providing the Melspectrogram image as an input toa first convolution layer and the output of the first convolution layeris provided as an input to a first max pooling 2-D layer. The first maxpooling 2-D layer generates an output image with further modifieddimensions, which is provided as an input to a second convolution layerand output of the second convolution layer is provided as an input to asecond max pooling 2-D layer. The output of the second max pooling 2-Dlayer is provided as an input to a third convolution layer and output ofthe third convolution layer is provided as an input to the third maxpooling 2-D layer. Therefore, multiple Melspectrogram images associatedwith different subjects are provided for training the deep learningmodel and the deep learning model is trained based on providingdifferent training datasets.

In an embodiment of the present invention, subsequent to training of thedeep learning model, the trained deep learning model is implemented forcomputing the identity the subject. A Melspectrogram image associatedwith a subject is passed through the trained deep learning model forcomputing the identity of the subject. The trained deep learning modelis configured to extract features associated with the Melspectrogramimage into a feature map to obtain predicted labels associated with thesubject. The predicted label are obtained based on labels used fortraining the deep learning model and for computing the identity of thesubject. The trained deep learning model classifies the Melspectrogramimage associated with the subject based on the trained labels in orderto identify the subject. In an embodiment of the present invention, theinput Melspectrogram image, associated with the subject is pre-processedfor computing a Melspectrogram image of a dimension “None, 32, 32, 3”which is provided as an input to the first convolution layer of the deeplearning model. The dimension ‘None’ represents various numbers ofimages which are provided while training and therefore represents abatch size. “32, 32, and 3” represents height (H1), width (W1) and depth(D1) respectively of the Melspectrogram image. The first convolutionlayer thereafter generates an output of a dimension “None, 30, 30, 16”.In an exemplary embodiment of the present invention, the output isgenerated by first convolution layer based on the following computation:as the input Melspectrogram image is of the dimension “None, 32, 32, 3”and if the number of filters (K1) used in first convolution layer is 16,strides (S) is 1 and spatial extent of filters (F) is 3 with 0 padding(P), then the output generated by first convolution layer is computedas: H1=(W1−F+2*P)/S+1; W1=(W1−F+2*P)/S+1; and D1=K1. Thus, H1=(32−3+1*0)/1+1=30; W1=(32−3+1*0)/1+1=30; and D1=16. Strides represent aparameter of the neural network's filter that modifies the amount ofmovement over the image pixel. Therefore, the output generated by thefirst convolution layer is of the dimension “None, 30, 30, 16”. Further,the output from the first convolution layer is provided as an input tothe first max pooling 2-D layer, which uses a shape of dimensions (2, 2)(i.e. (H, W)), for reducing the dimensions of the output received fromthe first convolution layer for generating an output of a dimension“None, 15, 15, 16” such that, wherein ‘None’ represents various numbersof images which are provided while training and 15, 15 and 16 representsheight, width and depth of the output image. Further, the output fromthe first max pooling 2-D layer is provided as an input to the secondconvolution layer. The second convolution layer generates an output of adimension (None, H2, W2, D2) based on the following computation:H2=(15'3+1*0)/1+1=13; W2=(15−3+1*0)/1+1=13; and D2=16. Therefore, theoutput generated by the second convolution layer is of a dimension“None, 13, 13, 16” such that, “None” represents various numbers ofimages which are provided while training and “13, 13, 16” representsheight (H2), width (W2) and depth (D2) of the output. Further, theoutput from the second convolution layer is provided as an input to thesecond max pooling 2-D layer, which reduces the dimension of the inputfor generating an output of a dimension “None, 6, 6, 16”, such that,“None” represents various numbers of images which are provided whiletraining and “6, 6, 16” represents height, width and depth of theoutput. Similarly, the output from the second max pooling 2-D layer isprovided to the third convolution layer for generating an output of adimension “None, 4, 4, 16”, wherein ‘None’ represents various numbers ofimages which are provided while training and “4, 4, 16” representsheight (H3), width (W2) and depth (D3) of the output. Further, theoutput from the third convolution layer is provided as an input to thethird max pooling 2-D layer, for generating an output of a dimension“None, 2, 2, 16”, such that “None” represents various numbers of imageswhich are provided while training and “2, 2, 16” represents height,width and depth of the output. The output from the third max pooling 2-Dlayer is provided to the flattening layer, which multiplies thedimensions together as (2*2* 16) for generating an output of a dimension“None, 64”. The output from the flattening layer is provided as an inputto the first dense layer, for generating an output of a dimension “None,256”. Further, the output from the first dense layer is provided as aninput to the dropout layer for generating an output of a dimension“None, 256”. Further, the output from the dropout layer is provided asan input to the second dense layer for generating an output of adimension “None, 20”, and value “20” represents number of labels.Further, the output from the second dense layer is associated with thepredicted label used for identifying the subject. The predicted labelsrepresent response of the trained deep learning model for classificationof the Melspectrogram image associated with the new subject.

In an embodiment of the present invention, parameters (Param#) arecomputed by implementation of the first convolution layer, the secondconvolution layer, the third convolution layer, the first dense layerand the second dense layer of the trained deep learning model. Theparameters represent the number of learnable elements in a convolutionlayer and are also computed during the training of the deep learningmodel prior to the implementation of the trained deep learning model. Inan embodiment of the present invention, the number of parameters arecomputed based on the number of filters (K) used along with their kernelsize (KZ), a bias and number of filters in the previous layer (D) usingthe following computation: Number of Parameters (Param#)=K *(D*(KZ)+1).In an exemplary embodiment of the present invention, if in the firstconvolution layer the number of filters (K) are 16 and the kernel size(KZ) is (3, 3), then the number of parameters for the first convolutionlayer is: Param #1=16*(3*(3*3)+1)=448. Further, for the secondconvolution layer, the number of parameters (Param #2) is: Param#2=16*(16*(3*3)+1)=2320. Yet further, for the third convolution layer,the number of parameters (Param #3) is: Param #3=16*(16*(3*3)+1)=2320.For the first dense layer, the number of parameters (Param #4) is: Param#4=256*(64+1)=16640; and further for second dense layer, the number ofparameters (Param #5) is: Param #5=20*(256+1)=5140. Therefore, number oftotal parameters is 26,868 (i.e. 448+2320+2320+16640+5140), and numberof trainable parameters is 26,868. The trainable parameters representthe parameters used for training the deep learning model.

In an embodiment of the present invention, the output from the seconddense layer associated with a subject's Melspectrogram image (e.g.subject 1 and a subject 2) is transformed in a feature map for obtainingpredicted labels by implementing the convolution layer filters presentin the convolution layer of the trained deep learning model in order toidentify the subject. In an embodiment of the present invention, thetrained deep learning model is applied for different subjects foridentifying and distinguishing one subject (subject 1) from anothersubject (subject 2). The

Melspectrogram image of a particular subject is provided to theconvolution layers of the trained deep learning model, which is passedthrough the filters of the convolution layers, and provided as thefeature map for distinguishing and identifying the subject 1 and thesubject 2 respectively. The trained model provides accuracy andsensitivity in identifying and distinguishing one subject from anothersubject based on computing the identity.

At step 1012, identity data of the subject associated with theidentified subject is visualized. In an embodiment of the presentinvention, identity data associated with the computed identity of thesubject is transmitted to a database for storage and future retrieval.The database may be located locally or remotely. The database may belocated locally on a standalone smartphone, laptop, tablet, a desktopcomputer, etc. at the subject's end. The database may be locatedremotely on a cloud server. In an embodiment of the present invention,the stored subject's identity data is further retrieved using a userdevice. The user device may include, but is not limited to, asmartphone, a tablet, a smartwatch, a computed system and a laptop. Thesubject may download an application on the user device or use a webaddress (e.g. a Universal Resource Locator (URL)) in order to connect tothe database for retrieving, accessing and viewing the subject'sidentity data. Further, the application and the web address provides aGraphical User Interface (GUI) in a dashboard form for viewing thesubject's identity data. In an embodiment of the present invention, theuser device via the application or the web address is configured touniquely authorize each subject by registering and providing access tosubjects for viewing the stored identity data. Further, the subject'sidentity data may be accessed by at least, the subject itself, subject'sdoctor, subject's caretaker, an insurer or any other person related tosubject in order to correctly and effectively determine the identity ofthe subject.

In an embodiment of the present invention, health data of the subject iscompute based on the identity data of the subject. Further, the healthdata of a non-intended subject which may have been captured and taggedintentionally or unintentionally along with the health data of theintended subject is removed based on the identity data of the identifiedsubject. An intended subject is a subject, whose health data is requiredto be captured and a non-intended subject is a subject, whose healthdata is not required to be captured. Further, the health data capturedfrom an intended subject and a non-intended subject is distinguished forpreventing mixing of the health data of the intended subject and thenon-intended subject.

In an embodiment of the present invention, the identity data of theidentified subject is coupled with subject's biometric data (e.g.subject's retina scan, subject's fingerprints etc.) for providing adouble layer secure authentication. For example, an insurer may usesubject's biometric data for identifying the subject in order to provideany special offers, however, the biometric data of the said subject maybe manipulated by another subject and the benefits of the insurance maybe wrongly appropriated by the said another subject. Coupling ofsubject's identity data, computed in accordance with various embodimentsof the present invention, with the subject's biometric data preventssuch wrongful appropriation, as the identity data of a particularsubject is unique to the said subject and cannot be manipulated.

Advantageously, in accordance with various embodiments of the presentinvention, the system and method of the present invention providesefficient identification of a subject based on the capturedmicro-vibrations associated with the BCG signals generated by thesubject's body, which are unique for every subject. The system andmethod of the present invention further provides correct and accuratedetermination of health data of the subject based on the computedidentity of the subject. The system and method of the present inventionprovides effective removal of the health data of the non-intendedsubject, which may have been captured and tagged along with the healthdata of the intended subject, as the micro-vibrations are different forevery subject. Further, the system and method of the present inventionprovides a non-invasive and double layer secure authenticatingtechniques for capturing subject's health data based on coupling themicro-vibrations associated with ballistocardiographic (BCG) signalsgenerated by the subject's body with the subject's biometric data.Further, the system and method of the present invention surprisinglyprovides efficient tracking of any minor change in the subject'sMelspectrogram signatures based on a feedback received from the subjectby the deep learning model. The feedback is further used to reinforcethe weights in the deep learning model. Further, the system and methodof the present invention provides efficient collection of subject's BGGsignals irrespective of the subject's position (e.g. standing, sittingor lying). Furthermore, the system and method of the present inventionprovides for accurately capturing all distinctive characteristics of theBCG signal. The system and method of the present invention providescapturing the micro-vibrations generated by the subject's body in arobust manner, as it cannot be replicated. Yet further, the system andmethod of the present invention is cost effective.

FIG. 11 illustrates an exemplary computer system in which variousembodiments of the present invention may be implemented. The computersystem 1102 comprises a processor 1104 and a memory 1106. The processor1104 executes program instructions and is a real processor. The computersystem 1102 is not intended to suggest any limitation as to scope of useor functionality of described embodiments. For example, the computersystem 1102 may include, but not limited to, a programmedmicroprocessor, a micro-controller, a peripheral integrated circuitelement, and other devices or arrangements of devices that are capableof implementing the steps that constitute the method of the presentinvention. In an embodiment of the present invention, the memory 1106may store software for implementing various embodiments of the presentinvention. The computer system 1102 may have additional components. Forexample, the computer system 1102 includes one or more communicationchannels 1108, one or more input devices 1110, one or more outputdevices 1112, and storage 1114. An interconnection mechanism (not shown)such as a bus, controller, or network, interconnects the components ofthe computer system 1102. In various embodiments of the presentinvention, operating system software (not shown) provides an operatingenvironment for various software executing in the computer system 1102,and manages different functionalities of the components of the computersystem 1102.

The communication channel(s) 1108 allow communication over acommunication medium to various other computing entities. Thecommunication medium provides information such as program instructions,or other data in a communication media. The communication mediaincludes, but not limited to, wired or wireless methodologiesimplemented with an electrical, optical, RF, infrared, acoustic,microwave, Bluetooth or other transmission media.

The input device(s) 1110 may include, but not limited to, a keyboard,mouse, pen, joystick, trackball, a voice device, a scanning device,touch screen or any other device that is capable of providing input tothe computer system 1102. In an embodiment of the present invention, theinput device(s) 1110 may be a sound card or similar device that acceptsaudio input in analog or digital form. The output device(s) 1112 mayinclude, but not limited to, a user interface on CRT or LCD, printer,speaker, CD/DVD writer, or any other device that provides output fromthe computer system 1102.

The storage 1114 may include, but not limited to, magnetic disks,magnetic tapes, CD-ROMs, CD-RWs, DVDs, flash drives or any other mediumwhich can be used to store information and can be accessed by thecomputer system 1102. In various embodiments of the present invention,the storage 1114 contains program instructions for implementing thedescribed embodiments.

The present invention may suitably be embodied as a computer programproduct for use with the computer system 1102. The method describedherein is typically implemented as a computer program product,comprising a set of program instructions which is executed by thecomputer system 1102 or any other similar device. The set of programinstructions may be a series of computer readable codes stored on atangible medium, such as a computer readable storage medium (storage1114), for example, diskette, CD-ROM, ROM, flash drives or hard disk, ortransmittable to the computer system 1102, via a modem or otherinterface device, over either a tangible medium, including but notlimited to optical or analogue communications channel(s) 1108. Theimplementation of the invention as a computer program product may be inan intangible form using wireless techniques, including but not limitedto microwave, infrared, Bluetooth or other transmission techniques.These instructions can be preloaded into a system or recorded on astorage medium such as a CD-ROM, or made available for downloading overa network such as the internet or a mobile telephone network. The seriesof computer readable instructions may embody all or part of thefunctionality previously described herein.

The present invention may be implemented in numerous ways including as asystem, a method, or a computer program product such as a computerreadable storage medium or a computer network wherein programminginstructions are communicated from a remote location.

While the exemplary embodiments of the present invention are describedand illustrated herein, it will be appreciated that they are merelyillustrative. It will be understood by those skilled in the art thatvarious modifications in form and detail may be made therein withoutdeparting from or offending the scope of the invention.

We claim:
 1. A system for efficiently identifying a subject, the systemcomprising: a memory storing program instructions; a processorconfigured to execute instructions stored in the memory; and anidentification engine executed by the processor and configured to:segment micro-voltage digital signals into intervals of a pre-definedtime period; transform the segmented micro-voltage digital signals intoa frequency domain for computing on a Mel's scale, wherein the Mel'sscale provides a unique signature of the subject in the form of aMelspectrogram image; and pass the Melspectrogram image through atrained deep learning model, wherein features associated with theMelspectrogram image are extracted into a feature map for obtainingpredicted labels associated with the subject based on labels used duringtraining of the deep learning model for identifying the subject.
 2. Thesystem as claimed in claim 1, wherein the intervals of the pre-definedtime period comprises ±10 seconds or less.
 3. The system as claimed inclaim 1, wherein the identification engine comprises a computation unitexecuted by the processor and configured to receive the micro-voltagedigital signals from a data receiver unit and process the micro-voltagedigital signals for segmenting into intervals of the pre-defined timeperiod.
 4. The system as claimed in claim 1, wherein the identificationengine comprises a prediction unit executed by the processor andconfigured to generate the deep learning model using neural networksassociated with the deep learning techniques, and wherein the deeplearning techniques comprise a Deep Neural Network (DNN), a Long ShortTerm Memory Network (LSTM) and a Convolutional Neural Network (CNN). 5.The system as claimed in claim 1, wherein one or more pre-defined numberof neural network layers of the deep learning model are stacked togetherthrough which the Melspectrogram image is passed, and wherein thepre-defined number of neural network layers of the deep learning modelcomprises three convolution 2-D layers paired with three max pooling 2-Dlayers respectively, two dense layers, a flattening layer between thetwo dense layer and a dropout layer.
 6. The system as claimed in claim5, wherein the three convolution 2-D layers paired with the max pooling2-D layers are used for extracting features from the Melspectrogramimages and subsequently carrying out downsampling, and wherein thefeatures are associated with the subject.
 7. The system as claimed inclaim 5, wherein the prediction unit passes a 1-D tensor associated withthe Melspectrogram images through the flattening layer to the denselayer, and wherein the dropout layer between the two dense layersprevents the deep learning model from over fitting.
 8. The system asclaimed in claim 1, wherein the deep learning model is trained in aprediction unit of the identification engine with training datasetsprior to identifying the subject, the training datasets are generatedbased on captured micro-vibrations associated with multiple subjects ina resting position and converting the captured micro-vibrations into theMelspectrogram image.
 9. The system as claimed in claim 8, wherein thetraining datasets are pre-processed and inputted to the prediction unitof the identification engine along with labels for training the deeplearning model, the labels used in training the deep learning modelrepresent ground truth associated with each Melspectrogram image, andwherein the training datasets comprises input images associated with themultiple subjects, the input images are Melspectrogram images in a 4-Dformat with dimensions “batch_size, height, width, depth”, such that thebatch_size is a number of training datasets in one forward pass, height(H) is height of the image, width (W) is width of the image, and depth(D) is number of color channels of the image.
 10. The system as claimedin claim 9, wherein the prediction unit trains the deep learning modelby passing Melspectrogram images through the convolution layers and maxpooling 2-D layers along with the respective labels, and wherein thebatch_size of the output images remains same as that of the inputMelspectrogram images and height, weight and depth of the output imagechanges based on number of filters, kernels and padding of theconvolution layers.
 11. The system as claimed in claim 10, wherein thefilters of the convolution layer of the deep learning model compriseslight color regions and dark color regions such that the light colorregion in the filter represents a value ‘1’, and the dark color regionin the filter represents a value ‘0’.
 12. The system as claimed in claim10, wherein the prediction unit passes the Melspectrogram image throughthe three convolution 2-D layers paired with three max pooling 2-Dlayers by providing the Melspectrogram image as an input to a firstconvolution layer and output of the first convolution layer is providedas an input to a first max pooling 2-D layer, the output of the firstmax pooling 2-D layer is provided as an input to a second convolutionlayer and output of the second convolution layer is provided as an inputto a second max pooling 2-D layer, the output of the second max pooling2-D layer is provided as an input to a third convolution layer andoutput of the third convolution layer is provided as an input to a thirdmax pooling 2-D layer.
 13. The system as claimed in claim 12, whereinthe prediction unit passes the Melspectrogram image through the traineddeep learning model for computing the identity of the subject by:pre-processing the Melspectrogram image associated with the subject tocompute a Melspectrogram image of dimensions (None, 32, 32, 3), thedimension ‘None’ represents various numbers of images which are providedwhile training and “32, 32, and 3” represents height (H1), width (W1)and depth (D1) respectively of the Melspectrogram image; providing thecomputed Melspectrogram image as an input to the first convolution layerof the deep learning model to generate an output of dimension “None, 30,30, 16” based on number of filters (K1) in the first convolution layer,strides (S), spatial extent of the filters (F) and padding (p), whereinthe number of filters is 16, strides (S) is 1 and spatial extent offilters (F) is 3 with 0 padding (P); providing the output from the firstconvolution layer as an input to a first max pooling 2-D layer, thefirst max pooling 2-D layer uses a shape of dimensions (2, 2) forreducing dimensions of the output received from the first convolutionlayer to generate an output of a dimension “None, 15, 15, 16”, wherein‘None’ represents various numbers of images which are provided whiletraining and “15, 15, 16” represents height, width and depth of theoutput; providing the output from the first max pooling 2-D layer as aninput to the second convolution layer to generate an output of adimension “None, 13, 13, 16”, wherein ‘None’ represents various numbersof images which are provided while training and “13, 13, 16” representsheight (H2), width (W2) and depth (D2) of the output; providing theoutput from the second convolution layer to as an input to a second maxpooling 2-D layer to generate an output of a dimension “None, 6, 6, 16”,wherein ‘None’ represents various numbers of images which are providedwhile training and “6, 6, 16” represents height, width and depth of theoutput; providing the output from the second max pooling 2-D layer to athird convolution layer to generate an output of a dimension “None, 4,4, 16”, wherein ‘None’ represents various numbers of images which areprovided while training and “4, 4, 16” represents height (H3), width(W3) and depth (D3) of the output; providing the output from the thirdconvolution layer as an input to a third max pooling 2-D layer togenerate an output of a dimension “None, 2, 2, 16”, wherein ‘None’represents various numbers of images which are provided while trainingand “2, 2, 16” represents height, width and depth of the output;providing the output from the third max pooling 2-D layer as an input toa flattening layer to generate an output of a dimension “None, 64”;providing the output from the flattening layer as an input to a firstdense layer to generate an output of a dimension “None, 256”; providingthe output from the first dense layer as an input to a dropout layer togenerate an output of a dimension “None, 256”; providing the output fromthe dropout layer as an input to a second dense layer to generate anoutput of a dimension “None, 20”, wherein the value “20” representsnumber of the labels; and transforming the output from the second denselayer into a feature map using the convolution layer filters present inthe convolution layer for obtaining the predicted labels, foridentifying the subject, based on the labels used during training of thedeep learning model.
 14. The system as claimed in claim 13, wherein theoutput from the second dense layer is associated with the predictedlabels used for identifying the subject, and wherein the predictedlabels represent response of the trained deep learning model forclassifying the Melspectrogram image associated with the subject. 15.The system as claimed in claim 13, wherein the prediction unit isconfigured to compute parameters based on the first convolution layer,the second convolution layer, the third convolution layer, the firstdense layer and the second dense layer of the trained deep learningmodel, and wherein the parameters represent the number of learnableelements in a convolution layer, and wherein the number of parametersare computed based on the number of filters (K) used along with theirkernel size (KZ), a bias and number of filters in the previous layer(D).
 16. The system as claimed in claim 15, wherein the parameters arecomputed during the training of the deep learning model, prior to theimplementation of the trained deep learning model.
 17. The system asclaimed in claim 13, wherein the prediction unit is configured totransmit identity data of the subject associated with the computedidentity of the subject to a database in the identification engine forstorage and future retrieval, and wherein a user device is configured toconnect to the database for retrieving, accessing and viewing thesubject's identity data via a Graphical User Interface (GUI) of anapplication in the user device or via a GUI rendered via a web portal.18. The system as claimed in claim 1, wherein the system is configuredto compute health data of the subject based on identity data associatedwith the identity of the subject, and wherein the health data of anon-intended subject captured and tagged intentionally orunintentionally along with the health data of the intended subject isremoved based on the identity data of the identified subject, andwherein the health data captured from an intended subject and anon-intended subject is distinguished for preventing mixing of thehealth data of the intended subject and the non-intended subject. 19.The system as claimed in claim 1, wherein the system is configured tocouple identity data associated with the identified subject with thesubject's biometric data for providing double layer secureauthentication, wherein the subject's biometric data comprises retinascan and fingerprints.
 20. A method for efficiently identifying asubject, wherein the method is implemented by a processor executinginstructions stored in a memory, the method comprises: segmentingmicro-voltage digital signals into intervals of a pre-defined timeperiod; transforming the segmented micro-voltage digital signals into afrequency domain for computing on a Mel's scale, wherein the Mel's scaleprovides a unique signature of the subject in the form of aMelspectrogram image; and passing the Melspectrogram image through atrained deep learning model, wherein features associated with theMelspectrogram image are extracted into a feature map for obtainingpredicted labels associated with the subject based on labels used duringtraining of the deep learning model for identifying the subject.
 21. Themethod as claimed in claim 20, wherein the intervals of the pre-definedtime period comprises ±10 seconds.
 22. The method as claimed in claim20, wherein the deep learning model is generated using neural networksassociated with the deep learning techniques, and wherein the deeplearning techniques comprises a Deep Neural Network (DNN), a Long ShortTerm Memory Network (LSTM) and a Convolutional Neural Network (CNN). 23.The method as claimed in claim 20, wherein one or more pre-definednumber of neural network layers of the deep learning model are stackedtogether, through which the Melspectrogram image is passed, and whereinthe pre-defined number of neural network layers of the deep learningmodel comprises three convolution 2-D layers paired with three maxpooling 2-D layers respectively, two dense layers, a flattening layerbetween the two dense layer and a dropout layer.
 24. The method asclaimed in claim 23, wherein the three convolution 2-D layers pairedwith the max pooling 2-D layers are used for extracting features fromthe Melspectrogram images and subsequently carrying out downsampling,and wherein the features are associated with the subject.
 25. The methodas claimed in claim 20, wherein a 1-D tensor associated with theMelspectrogram images is passed through the flattening layer to thedense layer, and wherein the dropout layer between two dense layersprevents the deep learning model from over fitting.
 26. The method asclaimed in claim 20, wherein the deep learning model is trained withtraining datasets prior to identifying the subject, the trainingdatasets are generated based on capturing the subject's micro-vibrationsassociated with the multiple subjects in a resting position andconverting the captured micro-vibrations into the Melspectrogram image.27. The method as claimed in claim 26, wherein the training datasets arepre-processed and inputted along with labels for training the deeplearning model, the labels used in training of the deep learning modelrepresent ground truth associated with every Melspectrogram image, andwherein the training datasets comprises input images associated withmultiple subjects, the input images are Melspectrogram images in a 4-Dformat with dimensions “batch_size, height, width, depth”, such that thebatch_size is a number of training datasets in one forward pass; height(H) is height of the image; width (W) is width of the image; and depth(D) is number of color channels of the image.
 28. The method as claimedin claim 24, wherein the Melspectrogram images are passed through theconvolution layers and max pooling 2-D layers of the deep learning modelalong with the respective labels, and wherein the batch_size of theoutput image remains same as that of input Melspectrogram image andheight, weight and depth of the output image changes based on number offilters, kernels and padding of the convolution layers.
 29. The methodas claimed in claim 23, wherein the Melspectrogram image is passedthrough the three convolution 2-D layers paired with three max pooling2-D layers by providing the Melspectrogram image as an input to a firstconvolution layer and output of the first convolution layer is providedas an input to a first max pooling 2-D layer, the output of the firstmax pooling 2-D layer is provided as an input to a second convolutionlayer and output of the second convolution layer is provided as an inputto a second max pooling 2-D layer, the output of the second max pooling2-D layer is provided as an input to a third convolution layer andoutput of the third convolution layer is provided as an input to a thirdmax pooling 2-D layer.
 30. The method as claimed in claim 29, whereinthe Melspectrogram image is passed through the trained deep learningmodel for computing the identity of the subject by: pre-processing theMelspectrogram image associated with the subject to compute aMelspectrogram image of dimensions (None, 32, 32, 3), the dimension‘None’ represents various numbers of images which are provided whiletraining and “32, 32, and 3” represents height (H1), width (W1) anddepth (D1) respectively of the Melspectrogram image; providing thegenerated Melspectrogram image as an input to the first convolutionlayer of the deep learning model to generate an output image ofdimension “None, 30, 30, 16” based on number of filters (K1) in thefirst convolution layer, strides (S), spatial extent of the filters (F)and padding (p), wherein the number of filters is 16, strides (S) is 1and spatial extent of filters (F) is 3 with 0 padding (P); providing theoutput from the first convolution layer as an input to a first maxpooling 2-D layer, the first max pooling 2-D layer uses a shape ofdimensions (2, 2) for reducing dimensions of the output image receivedfrom the first convolution layer to generate an output of a dimension“None, 15, 15, 16”, wherein ‘None’ represents various numbers of imageswhich are provided while training and “15, 15, 16” represents height,width and depth of the output; providing the output from the first maxpooling 2-D layer as an input to the second convolution layer togenerate an output of a dimension “None, 13, 13, 16”, wherein ‘None’represents various numbers of images which are provided while trainingand “13, 13, 16” represents height (H2), width (W2) and depth (D2) ofthe output image; providing the output from the second convolution layerto as an input to a second max pooling 2-D layer to generate an outputof a dimension “None, 6, 6, 16”, wherein ‘None’ represents variousnumbers of images which are provided while training and “6, 6, 16”represents height, width and depth of the output; providing the outputfrom the second max pooling 2-D layer to a third convolution layer togenerate an output of a dimension “None, 4, 4, 16”, wherein ‘None’represents various numbers of images which are provided while trainingand “4, 4, 16” represents height (H3), width (W3) and depth (D3) of theoutput; providing the output from the third convolution layer as aninput to a third max pooling 2-D layer as an input to generate an outputof a dimension “None, 2, 2, 16”, wherein ‘None’ represents variousnumbers of images which are provided while training and “2, 2, 16”represents height, width and depth of the output; providing the outputfrom the third max pooling 2-D layer as an input to a flattening layerto generate an output of a dimension “None, 64”; providing the outputfrom the flattening layer as an input to a first dense layer to generatean output of a dimension “None, 256”; providing the output from thefirst dense layer as an input image to a dropout layer to generate anoutput of a dimension “None, 256”; providing the output from the dropoutlayer as an input to a second dense layer to generate an output of adimension “None, 20”, wherein the value “20” represents number oflabels; and transforming the output from the second dense layer into afeature map using the convolution layer filters present in theconvolution layer for obtaining the predicted labels, for identifyingthe subject, based on the labels used during training of the deeplearning model.
 31. The method as claimed in claim 30, wherein theoutput from the second dense layer is associated with the predictedlabels used for identifying the subject, and wherein the predictedlabels represent response of the trained deep learning model forclassifying the Melspectrogram image associated with the subject.
 32. Acomputer program product comprising: a non-transitory computer-readablemedium having computer program code stored thereon, thecomputer-readable program code comprising instructions that, whenexecuted by a processor, causes the processor to: segment micro-voltagedigital signals into intervals of a pre-defined time period; transformthe segmented micro-voltage digital signals into a frequency domain forcomputing on a Mel's scale, wherein the Mel's scale provides a uniquesignature of the subject in the form of a Melspectrogram image; and passthe Melspectrogram image through a trained deep learning model, whereinfeatures associated with the Melspectrogram image are extracted into afeature map for obtaining predicted labels associated with the subjectbased on labels used during training of the deep learning model foridentifying the subject.